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Measurement basis

As explained in Quantum Programming in Depth

A measurement basis is a pair of orthogonal quantum states that defines the possible outcomes of a quantum measurement.

A measurement basis is an orthogonal pair of quantum states, |m₀⟩ and |m₁⟩, assigned to measurement outcomes 0 and 1. Choosing the basis determines how information about an unknown qubit state becomes an observable result. section 4.4.1

For distinguishing two nonorthogonal states, a basis can be parameterized and evaluated as follows:

  • Represent the input and basis states with angles: |ψ₁⟩ = cos(2θ)|0⟩ + sin(2θ)|1⟩, |m₀⟩ = sin(γ)|0⟩ − cos(γ)|1⟩, and |m₁⟩ = cos(γ)|0⟩ + sin(γ)|1⟩. section 4.4.1
  • Use outcome 0 to guess |ψ₀⟩ and outcome 1 to guess |ψ₁⟩, then calculate correctness from squared overlaps. section 4.4.1
  • For equally likely inputs, maximize P_success = ½[sin²(γ) + cos²(γ − 2θ)] by choosing γ. section 4.4.1

Minimum-error discrimination

Distinguish |ψ₀⟩ = |0⟩ from |ψ₁⟩ = ½|0⟩ + (√3/2)|1⟩. The state angle θ and basis angle γ describe how closely the orthogonal measurement states can align with the states being distinguished. section 4.4.1

Figure 4.6 Choosing the measurement basis for distinguishing \(\ket{\psi_0} = \ket{0}\) from \(\ket{\psi_1} = \frac12 \ket{0} + \frac{\sqrt3}{2} \ket{1}\) with minimum error. We can describe the state \(\ket{\psi_1}\) using only one parameter, the angle \(\theta\) (for this example, \(2\theta = \frac{\pi}{3}\)), and the measurement basis using the second parameter, the angle \(\gamma\). We want to choose the measurement basis states so that they are as close to the states being distinguished as possible while remaining orthogonal.
figure

A geometric diagram shows the two input states and the orthogonal measurement pair on a unit circle, making the angle-based optimization concrete. section 4.4.1

How Quantum Programming in Depth covers it

How deep it goes
Derives parameterized basis states, overlap probabilities, and an optimization objective, then points toward unequal priors and unambiguous discrimination.
Examples
Uses |0⟩ and ½|0⟩ + (√3/2)|1⟩, parameterized by θ and γ, to illustrate minimum-error basis selection.
What it assumes
Uses qubit state notation, orthogonality, amplitudes, inner products, and measurement probabilities while parameterizing states with angles.
What you can do after
Evaluate measurement choices by calculating correct-identification probabilities and maximizing their average.
How it connects
Connects quantum measurement with qubits, state discrimination, quantum state tomography, and unitary matrices.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Quantum Programming in Depth. The section links point to the parts of the book it draws on.